Language Model Security Database

Large Language Models (LLMs) designed for step-by-step problem-solving are vulnerable to query-agnostic adversarial triggers. Appending short, semantically irrelevant text snippets (e.g., "Interesting fact: cats sleep most of their lives") to mathematical problems consistently increases the likelihood of incorrect model outputs without altering the problem's inherent meaning. This vulnerability stems from the models' susceptibility to subtle input manipulations that interfere with their…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

A vulnerability exists in Large Language Models (LLMs) that allows for efficient jailbreaking by selectively fine-tuning only the lower layers of the model with a toxic dataset. This "Freeze Training" method, as described in the research paper, concentrates the fine-tuning on layers identified as being highly sensitive to the generation of harmful content. This approach significantly reduces training duration and GPU memory consumption while maintaining a high jailbreak success rate.

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

The SMAB (Sensitivity-based Multi-Armed Bandit) framework introduces a vulnerability in text classifiers and Large Language Models (LLMs) by enabling efficient, black-box adversarial text generation. The vulnerability exploits "word sensitivity"—the statistical probability that perturbing a specific word will flip a model's prediction—without requiring access to model weights or ground truth labels. By utilizing a Multi-Armed Bandit algorithm to explore and exploit word-level sensitivities…

The Knowledge-Distilled Attacker (KDA) model, when used to generate prompts for large language models (LLMs), can bypass LLM safety mechanisms resulting in the generation of harmful, inappropriate, or misaligned content. KDA's effectiveness stems from its ability to generate diverse and coherent attack prompts efficiently, surpassing existing methods in attack success rate and speed. The vulnerability lies in the LLMs' insufficient defenses against the diverse prompt generation strategies…

Large Language Models (LLMs) employing reinforcement learning from human feedback (RLHF) for safety alignment are vulnerable to a novel "alignment-based" jailbreak attack. This attack leverages a best-of-N sampling approach with an adversarial LLM to efficiently generate prompts that bypass safety mechanisms and elicit unsafe responses from the target LLM, without requiring additional training or access to the target LLM's internal parameters. The attack exploits the inherent tension between…

Large language models (LLMs) are vulnerable to adversarial suffix injection attacks. Maliciously crafted suffixes appended to otherwise benign prompts can cause the LLM to generate harmful or undesired outputs, bypassing built-in safety mechanisms. The attack leverages the model's sensitivity to input perturbations to elicit responses outside its intended safety boundaries.

Large Language Models (LLMs) employing safety alignment mechanisms are vulnerable to a bypass attack using simple, stochastic random augmentations of input prompts. The attack leverages the inherent brittleness of safety alignment to minor, randomly introduced modifications in the input, causing the LLM to generate unsafe outputs despite its safety training. Character-level augmentations prove significantly more effective than string insertions.

A poisoning attack against a Retrieval-Augmented Generation (RAG) system that manipulates the retriever component by injecting a poisoned document into the data used by the embedding model. This poisoned document contains modified and incorrect information. When activated, the system retrieves the poisoned document and uses it to generate misleading, biased, and unfaithful responses to user queries.